000			a
999			_c31341 _d31341
008			230205b xxu||||| |||| 00| 0 eng d
020			_a9781801819312
082			_a006.31 _bRAS
100			_aRaschka, Sebastian
245			_aMachine learning with PyTorch and Scikit-Learn : develop machine learning and deep learning models with Python
260			_aBirmingham : _bPackt Publishing _c2022
300			_axxviii, 741p.; _bill. _c24 cm
365			_b3899.00 _cINR _d1.00
504			_aIncludes bibliographical references and index.
520			_aThis book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book Description Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Explore frameworks, models, and techniques for machines to 'learn' from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you know some Python and you want to use machine learning and deep learning, pick up this book.
650			_aApprentissage automatique
650			_aData Mining
650			_aMachine learning
650			_aPython
650			_aComputer program language
650			_aAttention weights
650			_a Breast cancer wisconsin dataset
650			_aCar's fuel efficiency prediction project
650			_a Convolutional neural network(CNNs)
650			_aDeep learning
650			_a Ensemble methods
650			_aFeature transformation
650			_a Generative model
650			_a Holdout method
650			_a Iris dataset
650			_aMcCulloch-Pitts neuron model
650			_a No free lunch theorem
650			_a Output spectrum
650			_aPredictive model
650			_aRANdom sample-consensus (RANSAC
650			_aSigmoid function
650			_aWeight decays
650			_a XG Boost
650			_aZerio-shot tasks
700			_aLiu, Yuxi
700			_aMirjalili, Vahid
942			_2ddc _cBK